Haotian Fu (傅浩天)

Haotian Fu (傅浩天)

Computer Science PhD student at Brown University

Hi, I’m Haotian! I’m a Computer Science PhD student at Brown University, advised by Prof. George Konidaris. I also work closely with Prof. Michael Littman. I received my bachelor degree from Tianjin University, advised by Prof. Jianye Hao. I was a visiting student at UC Berkeley in 2018. I have also spent time as a research intern at Microsoft Research, as well as RAI Institute (Former Boston Dynamics AI Institute). I will join Google Deepmind as a research intern this summer.

Interests
  • LLM agents
  • Reinforcement Learning
  • Meta/continual Learning
  • Robotics

Publications

Haotian Fu, Yixiang Sun, Michael Littman, George Konidaris (2025). Knowledge Retention for Continual Model-Based Reinforcement Learning. In Forty-second International Conference on Machine Learning (ICML), 2025.

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Qi Zhao, Haotian Fu, Chen Sun, George Konidaris (2024). EPO:Hierarchical LLM Agents with Environment Preference Optimization. In The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024).

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Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris, Nicolas Le Roux, Marc-Alexandre Côté*, Xingdi Yuan* (2024). Language-guided Skill Learning with Temporal Variational Inference. In Forty-first International Conference on Machine Learning (ICML), 2024.

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Renhao Zhang, Haotian Fu, Yilin Miao, George Konidaris (2024). Model-based Reinforcement Learning for Parameterized Action Spaces. In Forty-first International Conference on Machine Learning (ICML), 2024.

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Haotian Fu, Shangqun Yu, Saket Tiwari, Michael Littman, George Konidaris (2023). Meta-learning Parameterized Skills. In Fortieth International Conference on Machine Learning (ICML), 2023.

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Haotian Fu, Jiayu Yao, Omer Gottesman, Finale Doshi-Velez, George Konidaris (2023). Performance Bounds for Model and Policy Transfer in Hidden-parameter MDPs. In Proceedings of the Eleventh International Conference on Learning Representations (ICLR), 2023.

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M. Baker, A. New, M. Aguilar-Simon, Z. Al-Halah, S. Arnold, E. Ben-Iwhiwhu, A. Brna, E. Brooks, R. Brown, Z. Daniels, A. Daram, F. Delattre, R. Dellana, E. Eaton, H. Fu, K. Grauman, J. Hostetler, S. Iqbal, D. Kent, N. Ketz, S. Kolouri, G. Konidaris, D. Kudithipudi, E. Learned-Miller, S. Lee, M. Littman, S. Madireddy, J. Mendez, E. Nguyen, C. Piatko, P. Pilly, A. Raghavan, A. Rahman, S. Ramakrishnan, N. Ratzlaff, A. Soltoggio, P. Stone, I. Sur, Z. Tang, S. Tiwari, K. Vedder, F. Wang, Z. Xu, A. Yanguas-Gil, H. Yedidsion, S. Yu, G. Vallabha (2023). A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. In Neural Networks, January 2023.

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Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris (2022). Model-based Lifelong Reinforcement Learning with Bayesian Exploration. In Advances in Neural Information Processing Systems 35 (NeurIPS), 2022.

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Haotian Fu, Hongyao Tang, Jianye Hao, Chen Chen, Xidong Feng, Dong Li, Wulong Liu (2021). Towards Effective Context for Meta Reinforcement Learning: an Approach based on Contrastive Learning. In AAAI 2021.

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Haotian Fu, Hongyao Tang, Jianye Hao, Wulong Liu, Chen Chen (2020). MGHRL: Meta Goal generation for Hierarchical Reinforcement Learning. In Beyond Tabula Rasa in Reinforcement Learning workshop at ICLR , 2020. In DAI 2020..

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Haotian Fu, Hongyao Tang, Jianye Hao, Zihan Lei, Yingfeng Chen, Changjie Fan (2019). Deep Multi Agent Reinforcement Learning with Discrete Continuous Hybrid Action Spaces. In IJCAI 2019.

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